# RNN 循环神经网络            LSTM
# 顺序类型的数据可以用此网络 例如：语音顺序

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

from tensorflow.contrib import rnn

# this is data
mnist = input_data.read_data_sets("MNIST_data",one_hot=True)

# hyperparameters
lr = 0.001
training_iters = 100000
batch_size = 128
display_step = 10

n_input = 28    # MNIST data input(image shape:28*28)
n_steps = 28    # time steps
n_hidden_unis = 128 #   随便设置多少 neurons in hidden layer
n_classes = 10  # MNIST classes(0-9 digits)

# tf Graph input
x = tf.placeholder(tf.float32,[None,n_steps,n_input])
y = tf.placeholder(tf.float32,[None,n_classes])

# Define weights
weights = {
    # (28,128)
    'in' : tf.Variable(tf.random_normal([n_input,n_hidden_unis])),
    # (128,10)
    'out' : tf.Variable(tf.random_normal([n_hidden_unis,n_classes]))
}

biases = {
    # (128,)
    'in': tf.Variable(tf.constant(0.1,shape=[n_hidden_unis,])),
    # (10,)
    'out': tf.Variable(tf.constant(0.1,shape=[n_classes,]))
}


def RNN(X,weights,biases):

    # hidden layer for input to cell
    # X(128 batch,28 steps,28 inputs) ==> (128*28,28 inputs)
    X = tf.reshape(X,[-1,n_input])
    X_in = tf.matmul(X,weights['in']) + biases['in']      # ==> (128 batch * 28steps,128 hidden)
    X_in = tf.reshape(X_in,[-1,n_steps,n_hidden_unis])      # ==> (128,28,128)


    # cell
    lstm_cell = rnn.BasicLSTMCell(n_hidden_unis,forget_bias=1.0,state_is_tuple=True)
    _init_state = lstm_cell.zero_state(batch_size,dtype=tf.float32)

    outputs,states = tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=_init_state,time_major=False)


    # hidden layer for output as the final results
    results = tf.matmul(states[1],weights['out']) + biases['out']



    return results


pred = RNN(x,weights,biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))
train_op = tf.train.AdamOptimizer(lr).minimize(cost)

correct_pred = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    step = 0
    while step * batch_size < training_iters:
        batch_xs,batch_ys = mnist.train.next_batch(batch_size)
        batch_xs = batch_xs.reshape([batch_size,n_steps,n_input])
        sess.run([train_op],feed_dict={
            x:batch_xs,
            y:batch_ys
        })
        if step % 20 == 0:
            print(sess.run(accuracy,feed_dict={
                x:batch_xs,
                y:batch_ys
            }))
        step += 1
